Knowledge and Information Systems

, Volume 51, Issue 2, pp 435–457 | Cite as

Markov logic networks for adverse drug event extraction from text

  • Sriraam NatarajanEmail author
  • Vishal Bangera
  • Tushar Khot
  • Jose Picado
  • Anurag Wazalwar
  • Vitor Santos Costa
  • David Page
  • Michael Caldwell
Regular Paper


Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.


Natural language processing Adverse drug event extraction Markov logic networks Statistical relational learning 



The authors gratefully acknowledge National Institute of Health Grant Number NIGMS 5R01GM097618 for the support.


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Sriraam Natarajan
    • 1
    Email author
  • Vishal Bangera
    • 1
  • Tushar Khot
    • 2
  • Jose Picado
    • 3
  • Anurag Wazalwar
    • 1
  • Vitor Santos Costa
    • 4
  • David Page
    • 2
  • Michael Caldwell
    • 5
  1. 1.Indiana UniversityBloomingtonUSA
  2. 2.University of Wisconsin-MadisonMadisonUSA
  3. 3.Oregon State UniversityCorvallisUSA
  4. 4.University of PortoPortoPortugal
  5. 5.Marshfield ClinicMarshfieldUSA

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